Method and apparatus for predicting analyte concentration

ABSTRACT

A method for predicting a concentration of an in vivo analyte includes obtaining a plurality of in vivo spectra of the in vivo analyte, determining a learning section for a concentration predicting algorithm for the analyte based on an unchanged section, during which the concentration of the analyte is not substantially changed, and a plurality of the in vivo spectra, and predicting the concentration of the in vivo analyte by using the concentration predicting algorithm based on a learned result of the learning section and an intrinsic spectrum of the in vivo analyte.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a divisional of U.S. patent application Ser. No.15/270,812, filed on Sep. 20, 2016, which claims priority to KoreanPatent Application No. 10-2015-0134872, filed on Sep. 23, 2015, and allthe benefits accruing therefrom under 35 U.S.C. § 119, the content ofwhich in its entirety is herein incorporated by reference.

BACKGROUND (a) Field

Exemplary embodiments of the invention relate to a method and anapparatus for predicting a concentration of an in vivo analyte from abiological signal.

(b) Description of the Related Art

For predicting a concentration of in vivo analyte from a biologicalsignal, a partial least squares (“PLS”) algorithm or a net analytesignal (“NAS”) algorithm may be employed. Further, after projecting anelectromagnetic wave such as an infrared ray or the like to a testsubject, an in vivo spectrum obtained by a result of an interactionbetween an analyte and the electromagnetic wave may be used in theabove-stated algorithms for predicting the concentration of the in vivoanalyte. Herein, the in vivo spectrum may be obtained by using anoptical method such as infra-red spectroscopy, a Raman spectroscopy, orthe like.

The PLS algorithm is a statistical modeling tool for obtaining acorrelation between a multivariate input variable (independent variable)and an output variable (dependent variable) by using data obtained froman experiment or the like. The PLS algorithm is applied to analyzationof the biological signal, an analyte concentration at a certain time maybe predicted from the biological signal by learning a spectral changedepending on the analyte concentration. The PLS algorithm uses theanalyte concentration at a plurality of times and spectra obtained atcorresponding times thereof to predict the analyte concentration fromthe biological signal, and it is desired to periodically relearn thespectral change depending on a change of the analyte concentration toprevent deterioration of predictability.

The NAS algorithm predicts the analyte concentration by learning anintrinsic spectrum of the analyte and a spectrum changing factorirrelevant to the analyte concentration. When the NAS algorithm isapplied to the analyzation of the biological signal, the analyteconcentration at other times may be predicted from a spectrum at aspecific time by learning the spectrum changing factor except for thechange of the analyte concentration in a time section during which theconcentration of the analyte is constantly maintained, and the intrinsicspectrum of the analyte obtained from the experiment. In other words,the NAS algorithm learns that the spectral change in the time section(learning section) during which the concentration of the analyte is notchanged is irrelevant to the change of the analyte concentration, andthen predicts the analyte concentration in the time section (predictingsection) that is other than the learning section by using this learnedinformation and the intrinsic spectrum of the analyte.

The above information disclosed in this Background section is only forenhancement of understanding of the background of the invention andtherefore it may contain information that does not form the prior artthat is already known in this country to a person of ordinary skill inthe art.

SUMMARY

In a net analyte signal (“NAS”) algorithm, a prediction accuracy of theanalyte concentration is improved in the case that changing factors ofthe biological signal in the learning section and in the predictingsection are identical to each other. However, the prediction accuracy ofthe concentration is deteriorated in the case that the changing factorof the biological signal that is not generated in the predicting sectionis additionally learned in the learning section, i.e., unnecessarylearning occurs. Further, in the case that an unlearned changing factorof the biological signal is generated in the predicting section, theprediction accuracy is also deteriorated. Therefore, it is veryimportant to control such an extraneous factor in order to improve theprediction accuracy of the concentration. Conventionally, severaltechniques for improving the prediction accuracy of the NAS algorithmhave been suggested. However, any method capable of controlling theextraneous factor effectively has not been researched.

Exemplary embodiments of the invention relate to a method and anapparatus for predicting an analyte concentration employing categorizinga section of in vivo spectra as a similar section by calculating asimilarity between the spectra and determining a learning section usinga section during which the in vivo analyte concentration is relativelyconstantly maintained in each similar section, and then predicting thein vivo analyte concentration in the similar section including thelearning section by using the learning section thereof, in an algorithm,such as the NAS algorithm and the like, for predicting the in vivoanalyte concentration by learning a change of the spectrum in thesection during which the in vivo analyte concentration is relativelyconstantly maintained.

According to an exemplary embodiment of the invention, a method forpredicting a concentration of an in vivo analyte includes obtaining aplurality of in vivo spectra of the in vivo analyte; determining alearning section of a concentration predicting algorithm for the in vivoanalyte based on an unchanged section, during which a concentration ofthe in vivo analyte is not substantially changed, and the in vivospectra; and predicting the concentration of the in vivo analyte byusing the concentration predicting algorithm based on a learned resultof the learning section and an intrinsic spectrum of the in vivoanalyte.

In an exemplary embodiment, the in vivo analyte may be at least one ofglucose, urea, lactate, triglyceride, protein, cholesterol, and ethanol.

In an exemplary embodiment, the in vivo analyte may be glucose and theunchanged section, during which the concentration of the in vivo analyteis not substantially changed, may be a fasting section.

In an exemplary embodiment, the in vivo spectrum may include at leastone of an absorption spectrum and a reflection spectrum of an infra-redray.

In an exemplary embodiment, the in vivo spectrum may include adispersion spectrum of a single wavelength electromagnetic wave.

In an exemplary embodiment, the obtaining the in vivo spectra of the invivo analyte may include obtaining the in vivo spectra continually at apredetermined time interval.

In an exemplary embodiment, the concentration predicting algorithm mayinclude a NAS algorithm.

In an exemplary embodiment, the determining the learning section mayinclude calculating a similarity between the in vivo spectra,determining a section having a high similarity as the similar section,and determining a section, during which the unchanged section and thesimilar section overlap each other, as the learning section.

In an exemplary embodiment, the calculating the similarity between thein vivo spectra comprises may include aligning baselines of at least twospectra for calculating similarities thereof among the in vivo spectra,and calculating a difference between the at least two in vivo spectra,the baselines of which are aligned.

In an exemplary embodiment, the predicting the concentration of the invivo analyte may include predicting the concentration of the in vivoanalyte in the similar section including the learning section when alength of the learning section is longer than a predetermined sectionlength.

In an exemplary embodiment, the predicting the concentration of the invivo analyte may include re-determining the learning section in thesimilar section when the length of the learning section is shorter thana predetermined length.

In an exemplary embodiment, the predicting the concentration of the invivo analyte may include displaying a message to inform a user that aconcentration prediction is unavailable when a length of the learningsection is shorter than a predetermined length.

In an exemplary embodiment, the in vivo analyte may be included in ahuman body, an animal, a mammal, a non-mammal, or a microorganism.

According to another exemplary embodiment of the invention, an apparatusfor predicting a concentration of an in vivo analyte, includes: aprocessor; and a memory, where the processor executes a program storedin the memory to: obtaining a plurality of in vivo spectra of the invivo analyte; determining a learning section of a concentrationpredicting algorithm for the in vivo analyte based on an unchangedsection ,during which a concentration of the in vivo analyte is notsubstantially changed, and the in vivo spectra; and predicting theconcentration of the in vivo analyte by using the concentrationpredicting algorithm based on the learned result of the learning sectionand an intrinsic spectrum of the in vivo analyte.

In an exemplary embodiment, the in vivo analyte may be at least one ofglucose, urea, lactate, triglyceride, protein, cholesterol, and ethanol.

In an exemplary embodiment, the in vivo analyte may be glucose, and theunchanged section, during which the concentration of the in vivo analyteis not substantially changed, may be a fasting section.

In an exemplary embodiment, the in vivo spectrum may include at leastone of an absorption spectrum and a reflection spectrum of an infra-redray.

In an exemplary embodiment, the in vivo spectrum may include adispersion spectrum of a single wavelength electromagnetic wave.

In an exemplary embodiment, when the processor performs the obtainingthe in vivo spectra of the in vivo analyte, the processor may performobtaining the in vivo spectra continually at the predetermined timeinterval.

In an exemplary embodiment, the concentration predicting algorithm mayinclude a NAS algorithm.

In an exemplary embodiment, when the processor performs the determiningthe learning section, the processor may perform: calculating asimilarity between the in vivo spectra, determining a section having ahigh similarity as the similar section; and determining a section,during which the unchanged section and the similar section overlap eachother, as the learning section.

In an exemplary embodiment, when the processor performs the calculatingof the similarity, the at least one processor may perform stepsincluding aligning baselines of at least two spectra for calculatingsimilarities thereof among the in vivo spectra, and calculating thedifference between the at least two in vivo spectra whose baselines arealigned.

In an exemplary embodiment, when the processor performs the predictingthe concentration of the in vivo analyte, the processor may performpredicting the concentration of the in vivo analyte in the similarsection including the learning section, in a case that a length of thelearning section is longer than a predetermined section length.

In an exemplary embodiment, when the processor performs the predictingthe concentration of the in vivo analyte, processor may performre-determining the learning section in the similar section, in a casethat a length of the learning section is shorter than a predeterminedlength.

In an exemplary embodiment, when the processor performs the predictingthe concentration of the in vivo analyte, the processor performsdisplaying a message to inform a user that the concentration predictionis unavailable, in a case that a length of the learning section isshorter than a predetermined length.

In an exemplary embodiment, the in vivo analyte may be included in ahuman body, an animal, a mammal, a non-mammal, or a microorganism.

In an exemplary embodiment, the apparatus for predicting the analyteconcentration may further include a communicator which receives the invivo spectra from an infra-red sensor or a laser sensor through a wiredor wireless network.

In an exemplary embodiment, the apparatus for predicting the analyteconcentration may further include an infra-red sensor which generatesthe in vivo spectra by radiating infra-red rays to a human body.

In an exemplary embodiment, the apparatus for predicting the in vivoanalyte concentration may further include a laser sensor which generatesthe in vivo spectra by radiating a laser to a human body.

According to exemplary embodiments of the invention, the in vivo analyteconcentration may be predicted precisely by predicting the in vivoanalyte concentration in the similar section, while using thedetermining of the section that no extraneous factor occurs as thesimilar section of a biological signal by determining an occurring pointof the extraneous factor such as the change of a measuring position orthe like through calculating the similarity between the biologicalsignals, and determining the section maintaining the in vivo analyteconcentration at the constant in each similar section as the learningsection.

BRIEF DESCRIPTION OF THE DRAWINGS

These and/or other features of the invention will become apparent andmore readily appreciated from the following detailed description ofembodiments thereof, taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 is a block diagram illustrating an exemplary embodiment of aconcentration predicting apparatus for predicting a concentration of ananalyte, according to the invention;

FIG. 2 is a flowchart illustrating an exemplary embodiment of a methodfor predicting a blood sugar level, according to the invention;

FIG. 3A and FIG. 3B are drawings illustrating an extraneous factor thatmay be generated in a fasting section according to an exemplaryembodiment of the invention;

FIG. 4 is a flowchart illustrating an exemplary embodiment of a methodfor determining a learning section of a predicting algorithm for theblood sugar level, according to the invention;

FIG. 5 is a graph illustrating an exemplary embodiment of a method for asimilarity analysis between in vivo spectra, according to the invention;

FIG. 6 is a graph illustrating a calculated similarity between in vivospectra according to an exemplary embodiment of the invention;

FIG. 7A and FIG. 7B illustrate a similar section determined based on asimilarity between in vivo spectra according to an exemplary embodimentof the invention;

FIG. 8 is a graph illustrating a fasting section according to anexemplary embodiment of the invention;

FIG. 9 is a graph illustrating the learning section determined accordingto an exemplary embodiment of the invention; and

FIG. 10 is a graph illustrating a result obtained by performing anexemplary embodiment of a concentration predicting algorithm accordingto the invention.

FIG. 11A and FIG. 11B are graphs illustrating a result obtained bypredicting a blood sugar level and the actual blood sugar levelaccording to an exemplary embodiment of the invention.

FIG. 12A is a graph illustrating an arterial blood sugar level of a ratin the case that an entire fasting section is determined as a learningsection.

FIG. 12B is a graph illustrating an arterial blood sugar level of therat predicted by an exemplary embodiment of a concentration predictingapparatus according to an the invention.

FIG. 13 is a block diagram illustrating an alternative exemplaryembodiment of a concentration predicting apparatus for predicting aconcentration of an analyte, according to the invention.

DETAILED DESCRIPTION

The invention now will be described more fully hereinafter withreference to the accompanying drawings, in which various embodiments areshown. This invention may, however, be embodied in many different forms,and should not be construed as limited to the embodiments set forthherein. Rather, these embodiments are provided so that this disclosurewill be thorough and complete, and will fully convey the scope of theinvention to those skilled in the art. Like reference numerals refer tolike elements throughout.

It will be understood that when an element is referred to as being “on”another element, it can be directly on the other element or interveningelements may be present therebetween. In contrast, when an element isreferred to as being “directly on” another element, there are nointervening elements present.

It will be understood that, although the terms “first,” “second,”“third” etc. may be used herein to describe various elements,components, regions, layers and/or sections, these elements, components,regions, layers and/or sections should not be limited by these terms.These terms are only used to distinguish one element, component, region,layer or section from another element, component, region, layer orsection. Thus, “a first element,” “component,” “region,” “layer” or“section” discussed below could be termed a second element, component,region, layer or section without departing from the teachings herein.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms, including “at least one,” unless the content clearly indicatesotherwise. “Or” means “and/or.” As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. It will be further understood that the terms “comprises”and/or “comprising,” or “includes” and/or “including” when used in thisspecification, specify the presence of stated features, regions,integers, steps, operations, elements, and/or components, but do notpreclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

“About” or “approximately” as used herein is inclusive of the statedvalue and means within an acceptable range of deviation for theparticular value as determined by one of ordinary skill in the art,considering the measurement in question and the error associated withmeasurement of the particular quantity (i.e., the limitations of themeasurement system). For example, “about” can mean within one or morestandard deviations, or within ±30%, 20%, 10%, 5% of the stated value.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs. It willbe further understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art and thepresent disclosure, and will not be interpreted in an idealized oroverly formal sense unless expressly so defined herein.

FIG. 1 is a block diagram illustrating an exemplary embodiment of aconcentration predicting apparatus for predicting a concentration of ananalyte, e.g., an in vivo analyte, according to the invention.

In an exemplary embodiment, the concentration predicting apparatus 100may be configured to predict the concentration of the analyte byanalyzing an in vivo spectrum thereof using a concentration predictingalgorithm for the analyte. Herein, the in vivo spectrum may be generatedwhen infer-red rays or laser beams (that is, single wavelengthelectromagnetic waves) are transmitted or diffusely reflected to anorganism, e.g., a human body, and then absorbed or dispersed by theanalyte therein, and may be obtained continually at a predetermined timeinterval. That is, the in vivo spectrum may include the absorptionspectrum and the reflection spectrum of the infra-red ray, or mayinclude the dispersion spectrum of the single wavelength electromagneticwaves. The in vivo spectrum may be applied to the concentrationpredicting algorithm after being obtained by an infra-red spectroscopyor a Raman spectroscopy. The in vivo spectrum may be one of anabsorption spectrum and a reflection spectrum of an infra-red ray or adispersion spectrum of a single wavelength electromagnetic wave. In suchan embodiment, the concentration predicting apparatus 100 may predictthe concentration of an in vivo analyte of a human body, an animal, amammal, non-mammal, or a microorganism. In such an embodiment, the invivo analyte may be at least one of glucose, urea, lactate,triglyceride, protein, cholesterol and ethanol.

In such an embodiment, where the in vivo analyte is glucose, theconcentration of the analyte may represent a blood sugar level, and atime section during which an analyte concentration is substantiallyconstantly maintained, may represent a fasting section. In such anembodiment, where the in vivo analyte is glucose, near infra-red (“NIR”)or middle infra-red (“MIR”) rays may be used for generating the in vivospectrum of glucose.

Referring to FIG. 1, an exemplary embodiment of the concentrationpredicting apparatus 100 includes a section determiner 110, a learningunit 120, and a concentration predictor 130.

In an exemplary embodiment, the section determiner 110 may be configuredto categorize sections of a plurality of in vivo spectra, each obtainedcontinually at a predetermined time interval, which may be about aone-minute time interval, as similar sections based on calculatedsimilarity between the plurality of in vivo spectra, and determine alearning section in each similar section based on a section during whichthe analyte concentration is substantially constantly maintained (forexample, a section during which no analyte such as glucose or the likeis introduced into the human body, like the fasting section). In such anembodiment, when a length of the learning section is shorter than thatof a predetermined section, e.g., a time duration of about 20 minutes toabout 30 minutes, the section determiner 110 may determine that aconcentration prediction is unavailable based on the learning sectionand display a message to inform a user that the concentration predictionis unavailable through an interface or a display device (not shown) orthe like in the concentration predicting apparatus 100.

In an exemplary embodiment, the learning unit 120 is configured to learna spectrum changing factor irrelevant to a change of the analyteconcentration based on an in vivo spectrum in each learning section thatis determined by the section determiner 110. In such an embodiment, thelearning unit 120 may learn the spectrum changing factor irrelevant tothe change of the analyte concentration by employing a principalcomponent analysis (“PCA”) method or the like.

In an exemplary embodiment, the concentration predictor 130 isconfigured to predict the analyte concentration by using an intrinsicspectrum of the analyte and a learned result of the learning section,e.g., the spectrum changing factor in each learning section. In such anembodiment, the concentration predictor 130 may predict an in vivoconcentration of the analyte by using a least square method. Then, theconcentration predictor 130 may deliver a predicted result of theanalyte through a communicator 140, or display the predicted result ofthe concentration to the user through the interface or the like, e.g., adisplay device, in the concentration predicting apparatus 100.

In an exemplary embodiment, a net analyte signal (“NAS”) algorithm maybe used for predicting the analyte concentration, e.g., the blood sugarlevel, and the concentration predicting apparatus may predict the bloodsugar level in the section that is other than the learning section(e.g., the predicting section in the similar section) by learning thatthe change of the in vivo spectrum in the fasting section is irrelevantto the change of the blood sugar level and analyzing the change of thein vivo spectrum. In a conventional concentration predicting apparatus,where an entire fasting section is determined as the learning section, achanging factor in the fasting section such as the extraneous factorresulted by the change of a position at which the in vivo spectrum isobtained in the fasting section or the like may be intactly reflected ona prediction of the blood sugar level, thereby deteriorating theprediction accuracy of the concentration. An exemplary embodiment of theconcentration predicting apparatus according to the invention mayimprove the prediction accuracy of the concentration by accuratelyrestricting the learning section and the predicting section (the sectionthat is other than the learning section in the similar section) bydetermining an occurring time of the extraneous factor such as thechange of the obtaining position of the spectrum or the like based onthe similarity between the in vivo spectra.

In an exemplary embodiment, where the in vivo spectrum is generated byan optical sensor disposed in an exterior of the concentrationpredicting apparatus, the concentration predicting apparatus 100 mayfurther include the communicator 140 configured to obtain the in vivospectrum from the optical sensor through a wired or wireless network. Insuch an embodiment, the communicator 140 may be configured to transmitthe predicted result of the analyte concentration to the outside of theconcentration predicting apparatus 100 through the wired or wirelesscommunication.

Alternatively, the concentration predicting apparatus 100 may furtherinclude an optical sensor 150 to obtain the in vivo spectrum.

In an exemplary embodiment, the concentration predicting apparatus maybe operated by a processor, a memory, and a transceiver. The memory maybe connected to the processor to store diverse information for operatingthe processor. The transceiver may be connected to the processor totransmit and receive wired or wireless signals to and from a terminal, aserver, or the like. The processor may be configured to implement afunction, a process, or a method according to an exemplary embodiment ofthe invention. An operation of an exemplary embodiment of theconcentration predicting apparatus according to the invention may beimplemented by the processor.

In an exemplary embodiment of the invention, the memory may be disposedin an interior or exterior of the processor, and may be connected to theprocessor by various already known means. The memory may be one ofvarious volatile and non-volatile storing media. In one exemplaryembodiment, for example, the memory may include a read-only memory(“ROM”) or a random access memory (“RAM”).

FIG. 2 is a flowchart illustrating an exemplary embodiment of a methodfor predicting a blood sugar level, according to the invention.

Referring to FIG. 2, in an exemplary embodiment, a plurality of the invivo spectra of the analyte in the human body is obtained continually(S201). In such an embodiment, a plurality of the in vivo spectra may beobtained continually at the predetermined time interval, e.g., aone-minute interval.

In such an embodiment, the section determiner 110 determines a learningsection for a concentration predicting algorithm based on a similaritybetween the spectra and a section during which an analyte concentrationis relatively constantly maintained (S202). In such an embodiment, thesection determiner 110 may effectively control the extraneous factorthat may affect the concentration prediction of the analyte, since theoccurrence of the extraneous factor may be determined based on thesimilarity of the in vivo spectra.

FIG. 3A and FIG. 3B illustrate the extraneous factor that may begenerated in the fasting section according to an exemplary embodiment ofthe invention.

FIG. 3A and FIG. 3B are graphs in which a horizontal axis indicates atime and a vertical axis indicates the blood sugar level, and a solidline thereof shows the change of the blood sugar level depending on thetime. FIG. 3A and FIG. 3B are graphs illustrating a case that theanalyte is glucose and the analyte concentration is the blood sugarlevel.

Referring to FIG. 3A, the blood sugar level of a user of an exemplaryembodiment of the concentration predicting apparatus is increased aftera meal time, and the time section or an unchanged section, during whichthe analyte concentration is not substantially changed, e.g., thefasting section, may be determined as a time section before meal time.

In such an embodiment, a significant change of the in vivo spectrum mayoccur in the fasting section by the changing factor, such as the changeof the obtaining position of the in vivo spectrum, a skin wash or thelike. In such an embodiment, the length of the learning section may bechanged depending on an occurrence of the changing factor in the fastingsection.

Referring to FIG. 3B, in such an embodiment, when a measuring positionof the in vivo spectrum is changed, the learning section is changed to ashorter section (section {circle around (1)}) than the fasting section,and when the measuring position of the in vivo spectrum is changed dueto the skin wash thereafter, the learning section may be changed to aneven shorter section (section {circle around (2)}). In such anembodiment, if the learning section is changed to an insufficientlyshort section (section {circle around (3)}) after the occurrence of thechanging factor in the fasting section is too close to the meal time,the prediction of the blood sugar level may be failed because of aninsufficiency of the learning section. In such an embodiment, when thelength of the learning section is shorter than the predetermined sectionlength, e.g., a time duration of about 20 minutes to about 30 minutes,the concentration prediction of the analyte in the similar sectionincluding the learning section may be determined to be unavailable. Insuch an embodiment, the concentration predictor 130 may predict theanalyte concentration in the similar section including the learningsection, when the length of the learning section is longer than that ofa predetermined section. In such an embodiment, the section determiner110 may inform the user that the concentration prediction is unavailablethrough an interface or the display device of the concentrationpredicting apparatus, since the length of the learning section isshorter than that of the predetermined section. If the learning section(section {circle around (4)}) in the similar section may be additionallydetermined by the analysis on the similarity between the spectra evenafter the meal time, the prediction of the blood sugar level in thesimilar section may be available. In an exemplary embodiment, asdescribed above, since the extraneous factor such as the change of themeasuring position of the in vivo spectrum, the skin wash or the likemay affect the prediction of the blood sugar level, the sectiondeterminer 110 may remove the extraneous factor by determining a similarsection, which is a continuous section having no extraneous factor,based on the similarity between the spectra.

FIG. 4 is a flowchart illustrating to an exemplary embodiment of amethod for determining a learning section of a predicting algorithm forthe blood sugar level, according the invention, FIG. 5 is a graphillustrating an exemplary embodiment of a method for a similarityanalysis between in vivo spectra, according to the invention, and FIG. 6is a graph illustrating a calculated similarity between in vivo spectraaccording to an exemplary embodiment of the invention.

In an exemplary embodiment, the section determiner 110 of theconcentration predicting apparatus calculates a similarity between invivo spectra to remove an extraneous factor such as a change of ameasuring position or the like from the prediction of the blood sugarlevel (S401). In such an embodiment, the section determiner 110categorizes sections of the in vivo spectra as similar sections based onthe calculated similarity (S402). In such an embodiment, the sectiondeterminer 110 may find an occurrence or occurring time of theextraneous factor by categorizing the sections of the in vivo spectra assimilar sections. In such an embodiment, the sections of the in vivospectrum may be categorized as the similar sections based on theoccurring time of the extraneous factor. In such an embodiment, thesection determiner 110 determines an unchanged section during which theanalyte concentration is not substantially changed in each similarsection as the learning section (S403). In such an embodiment, thelearning section may be determined as a section, during which thesimilar section and the unchanged section (e.g., the fasting section)overlap each other.

In an exemplary embodiment of the invention, a similarity between the invivo spectra may be calculated by a one-to-one comparison between the invivo spectra obtained continually.

FIG. 5 to FIG. 11 are graphs illustrating a predicted result of theblood sugar level of a rat. In the prediction of the blood sugar levelaccording to the exemplary embodiment of the invention, the blood sugarlevel is changed by injecting a high concentration glucose solution intoan artery of the rat and an infra-red spectrum of a skin thereof ismeasured at a time interval of about 1.2 minutes.

The horizontal axis of upper and lower graphs of FIG. 5 represents afrequency of the in vivo spectrum and the vertical axis thereofrepresents a magnitude of the in vivo spectrum. Referring to FIG. 5, theupper graph illustrates two infra-red spectra measured at differenttimes, and the lower graph illustrates the spectra whose baselines arealigned for a similarity calculation. In an exemplary embodiment of theinvention, as shown in FIG. 5, the similarity may be calculated based ona difference between two in vivo spectra after the baselines of the invivo spectra are aligned for the similarity calculation. In analternative exemplary embodiment of the invention, another method forcalculating the spectral change caused by the extraneous factor, forexample, calculating the difference between spectra without the baselinefitting, comparing the Fourier transforms result, and determining theextraneous factor by using changes of a specific spectrum (e.g., afrequency of about 4200 cm⁻¹), may be employed.

Referring to FIG. 6, the horizontal axis and the vertical axisrespectively represent measuring times of two spectra for calculatingthe similarity among continually measured spectra, and the calculatedsimilarity is illustrated by a shade. As shown in FIG. 6, an in vivospectrum measured at about 1 hour may be similar to an in vivo spectrummeasured between about zero (0) hour and about 2 hours, but may bedissimilar to an in vivo spectrum measured between about 2 hours andabout 7 hours. As shown in FIG. 6, an in vivo spectrum measured at about5 hours may be dissimilar to an in vivo spectrum measured between aboutzero (0) hour and about 3 hours, but may be similar to an in vivospectrum measured between about 3 hours and about 7 hours.

In such an embodiment, the section determiner 110 of the concentrationpredicting apparatus determines the similar sections based on thecalculated similarity. FIG. 7A and FIG. 7B illustrate similar sectionsdetermined based on the similarity between in vivo spectra shown in FIG.6 according to an exemplary embodiment of the invention.

FIG. 7A is a graph illustrating a difference between an in vivo spectrummeasured at about 1 hour and an in vivo spectrum measured at a differenttime, and FIG. 7B is a graph illustrating a difference between an invivo spectrum measured at about 5 hours and an in vivo spectrum measuredat a different time. In FIG. 7A and FIG. 7B, the horizontal axisrepresents a measuring time of the in vivo spectrum, the vertical axisof FIG. 7A represents the difference between the in vivo spectrummeasured at about 1 hour and the in vivo spectrum measured at thedifferent time, and the vertical axis of FIG. 7B represents thedifference between the in vivo spectrum measured at about 5 hours andthe in vivo spectrum measured at the different time.

Referring to FIG. 7A, since the in vivo spectrum measured at about 1hour is similar to an in vivo spectrum measured between about 20 minutesand about 2 hours, a section from about 20 minutes to about 2 hours maybe determined as the similar section. Referring to FIG. 7B, since the invivo spectrum measured at about 5 hours is similar to an in vivospectrum measured between about 3 hours 20 minutes and about 6 hours 55minutes, a section from about 3 hours 20 minutes to about 6 hours 55minutes may be determined as a similar section having a high similaritybetween the in vivo spectra. Herein, the similarity may be changeddepending on a predetermined threshold when the similar section isdetermined. Further, the biological signal such as a temperature, apressure, an impedance of the skin, or the like may be used additionallywhen the similar section is determined.

In such an embodiment, the section determiner 110 may determine asection, during which the similar section and the unchanged sectionoverlap each other, as the learning section.

FIG. 8 is a graph illustrating a fasting section according to anexemplary embodiment of the invention, and FIG. 9 is a graphillustrating a learning section determined according to an exemplaryembodiment of the invention.

Referring to FIG. 8, a solid line therein indicates curves illustratingthe change of the blood sugar level with respect to time, and theunchanged section, during which the blood sugar level (i.e., the in vivoconcentration of glucose) is not substantially changed, may bedetermined as the fasting section in FIG. 8. The fasting sectionillustrated in FIG. 8 includes sections from about 1 hour to about 3hours and from about 6 hours to about 6.5 hours. In an exemplaryembodiment, the fasting section may be determined as an interval during(e.g., throughout) which the actual blood sugar level is maintainedsubstantially constant when the actual blood sugar level is sensed every10 minute interval. In an alternative exemplary embodiment, a user ofthe concentration predicting apparatus may input the fasting section,which may be measured manually by the user or predetermined according toa life cycle of the user, into the concentration predicting apparatus.In an exemplary embodiment, for example, the fasting section may bedetermined based on an eating time. In such an embodiment, where theeating time is used for determining the fasting section, an intervalfrom a time point after a predetermined time from a first eating timepoint to a second eating time point may be determined as the fastingsection. In such an embodiment, in a case where a user has a breakfastat 8 AM and a lunch at 1 PM, and the predetermined time is 3 hours, thefasting section is for 2 hours from 11 AM to 1 PM.

Further, referring to FIG. 9, the learning section, during which theunchanged section, during which the analyte concentration is notsubstantially changed, and the similar section, during which the in vivospectra are similar to each other, overlap each other, includes sectionsfrom about 1 hour to about 2 hours (a first learning section), fromabout 2.5 hours to about 3 hours (a second learning section), and fromabout 6 hours to about 6.5 hours (a third learning section).

In such an embodiment, the learning section may be applied to anotheralgorithm for predicting the analyte concentration by learning that thechange of the in vivo spectrum in the unchanged section, during whichthe analyte concentration is not substantially changed, is irrelevant tothe analyte, in addition to the NAS algorithm.

Referring back to FIG. 1 and FIG. 2, in an exemplary embodiment, thelearning unit 120 learns each learning section (S203). In one exemplaryembodiment, for example, the learning unit 120 may learn the spectrumchanging factor irrelevant to the change of the analyte concentrationbased on the in vivo spectrum in each learning section. In an exemplaryembodiment, the concentration predictor 130 predicts the analyteconcentration in the similar section based on learned results of thelearning sections and the intrinsic spectrum of the analyte (S204).

FIG. 10 is a graph illustrating a result obtained by performing aconcentration predicting algorithm according to an exemplary embodimentof the invention.

Referring to FIG. 10, three curves illustrating performed results of theconcentration predicting algorithm related to each learning section (thefirst, second, and third learning sections) are shown. Since each curveillustrated in FIG. 10 is valid only in the similar section including acorresponding learning section, a final predicted result may be acquiredby combining the curves based on the similar section.

FIG. 11A and FIG. 11B illustrate a result obtained by predicting a bloodsugar level and the actual blood sugar level according to an exemplaryembodiment of the invention.

In FIG. 11A and FIG. 11B, the horizontal axis represents time, and thevertical axis represents the blood sugar level. The change of apredicted blood sugar level with respect to time and the change of theactual blood sugar level with respect to time are illustrated. In thesections between about zero (0) hour and about 20 minutes, between about2 hours and about 2.5 hours, and between about 6.5 hours and about 8hours in FIG. 11A, a blood sugar level may not be effectively predicteddue to the insufficiency of the learning section. FIG. 11B illustrates aresult of adopting about +15 minutes of a delay to FIG. 11A based on anartery-skin delay in the change of the blood sugar level. That is, sincethe change of the blood sugar level in the artery is reflected oncutaneous tissue with a delay of about 15 minutes, the result thatmatches an actual blood-sugar curve in a range of less than about 17millimoles per deciliter (mM/dL) of the blood sugar level may beacquired as illustrated in FIG. 11B when considering the delay of about15 minutes.

FIG. 12A is a graph illustrating an arterial blood sugar level of a ratin the case that an entire fasting section is determined as a learningsection, and FIG. 12B is a graph illustrating an arterial blood sugarlevel of the rat predicted by a concentration predicting apparatusaccording to an exemplary embodiment of the invention.

The horizontal axes of FIG. 12A and FIG. 12B represent an arterial bloodsugar level that are actually measured in the range of less than about17 mM/dL, and the vertical axes thereof represent the arterial bloodsugar level that are predicted in the range of less than about 17 mM/dL.That is, as small circles representing compared results are distributedmore tightly to around a x-y line in FIG. 12A and FIG. 12B, predictedarterial blood sugar levels are coincide with actual arterial bloodsugar levels, thereby showing an high accuracy of a concentrationpredicting method according to an exemplary embodiment of the invention.

Referring to FIG. 12A, in a conventional concentration predicting methodwhere the entire fasting section is determined as the learning section,the predicted arterial blood sugar level shows a slight deviation fromthe actual arterial blood sugar level. However, referring to FIG. 12B,the predicted arterial blood sugar level is considerably coincide withthe actual arterial blood sugar level in the range of less than about 17mM/dL by the result of accurately determining the learning section basedon the similar section and the fasting section by the concentrationpredicting apparatus according to the exemplary embodiment of theinvention. That is, FIG. 12B shows that the concentration predictingapparatus according to an exemplary embodiment of the invention mayperform a more accurate concentration prediction than that of theconventional concentration predicting method.

Therefore, in such an embodiment, the concentration predicting apparatusfor predicting the concentration of the analyte may effectively controlthe extraneous factor in predicting the concentration of the in vivoanalyte by determining the similar section of the spectrum throughcalculation of the similarity between the in vivo spectrum, and thendetermining the learning section based on the determined similar sectionand the section during which the analyte concentration is substantiallymaintained. In such an embodiment, the concentration predictingapparatus may improve an accuracy of the concentration prediction bypredicting the analyte concentration in the similar section includingthe learning section that is determined by the concentration predictingapparatus.

FIG. 13 is a block diagram illustrating an alternative exemplaryembodiment of a concentration predicting apparatus for predicting aconcentration of an analyte, according to the invention.

Referring to FIG. 13, an alternative exemplary embodiment of aconcentration predicting apparatus for predicting a concentration of ananalyte may be an interactive network analyzing apparatus 1300 includinga processor 1310 and a memory 1320. The processor 1310 may be configuredto implement the function, the process, or the method of an exemplaryembodiment of the invention described herein. The memory 1320 may beconnected to the processor 1310 to store diverse information foroperating the processor 1310 or may store at least one program performedby the processor 1310. In such an embodiment, the processor 1310 may beconfigured to operate the interactive network analyzing apparatus 1300.

In an exemplary embodiment of the invention, the memory may be disposedin the interior or exterior of the processor, and may be connected tothe processor by various known means. The memory may be one of variousvolatile and non-volatile storing media. In one exemplary embodiment,for example, the memory may include the ROM or the RAM.

While the invention has been described in connection with what ispresently considered to be practical exemplary embodiments, it is to beunderstood that the invention is not limited to the disclosedembodiments, but, on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

What is claimed is:
 1. A method for predicting a concentration of an invivo analyte, the method comprising: obtaining a plurality of in vivospectra of the in vivo analyte; determining a learning section of aconcentration predicting algorithm for the in vivo analyte based on anunchanged section, during which a concentration of the in vivo analyteis not substantially changed, and the in vivo spectra; and predictingthe concentration of the in vivo analyte by using the concentrationpredicting algorithm based on a learned result of the learning sectionand an intrinsic spectrum of the in vivo analyte.
 2. The method of claim1, wherein the in vivo analyte is at least one of glucose, urea,lactate, triglyceride, protein, cholesterol, and ethanol.
 3. The methodof claim 1, wherein, the in vivo analyte is glucose, and the unchangedsection, during which the concentration of the in vivo analyte is notsubstantially changed, is a fasting section.
 4. The method of claim 1,wherein the in vivo spectrum comprise at least one of an absorptionspectrum or a reflection spectrum of an infra-red ray.
 5. The method ofclaim 1, wherein the in vivo spectrum comprise a dispersion spectrum ofa single wavelength electromagnetic wave.
 6. The method of claim 1,wherein the obtaining the in vivo spectra of the in vivo analytecomprises obtaining the in vivo spectra continually at a predeterminedtime interval.
 7. The method of claim 1, wherein the concentrationpredicting algorithm comprises a net analyte signal algorithm.
 8. Themethod of claim 1, wherein the determining the learning sectioncomprises: calculating a similarity between the in vivo spectra;determining a section having a high similarity as a similar section; anddetermining a section, during which the unchanged section and thesimilar section overlap each other, as the learning section.
 9. Themethod of claim 8, wherein the calculating the similarity between the invivo spectra comprises: aligning baselines of at least two spectra forcalculating similarities thereof among the in vivo spectra; andcalculating a difference between the at least two in vivo spectra, thebaselines of which are aligned.
 10. The method of claim 1, wherein thepredicting the concentration of the in vivo analyte comprises predictingthe concentration of the in vivo analyte in the similar sectionincluding the learning section when a length of the learning section islonger than a predetermined section length.
 11. The method of claim 1,wherein the predicting the concentration of the in vivo analytecomprises re-determining the learning section in the similar sectionwhen a length of the learning section is shorter than a predeterminedlength.
 12. The method of claim 1, wherein the predicting theconcentration of the in vivo analyte comprises displaying a message toinform a user that a concentration prediction is unavailable when alength of the learning section is shorter than a predetermined length.13. The method of claim 1, wherein the in vivo analyte is included in ahuman body, an animal, a mammal, a non-mammal or a microorganism.